Maintaining security is a constant concern for numerous installations including military installations, government installations, international borders, civilian installations, and the like. In an installation having high traffic, e.g., many vehicles including automobiles, trains, trucks, and/or the like moving in and out during the course of a typical day, a significant concern is whether one or more of these vehicles is being used to transport contraband, such as explosive devices, weapons, smuggled individuals, and/or the like.
Currently, at high-security installations, a visual inspection for potential threats is performed on incoming (or exiting) vehicles. For the most part, this inspection is done by humans equipped with small cameras and/or mirrors to inspect the undercarriage of the vehicle. However, some visual inspections utilize remotely operated vehicles (ROVs). In either case, the visual inspection is time-consuming work and exposes the inspector to grave danger when a vehicle being used to transport contraband attempts to enter (or exit) the installation. Some commercial systems seek to automate aspects of the visual inspection of vehicles. These systems require slow-speed entry/exit, almost entirely rely on the use of visible light, and often require a large, unwieldy speed-bump configuration and/or an entire separate structure to perform the inspection. Further, many approaches continue to rely completely on human perceptions, with several current commercial inspection systems providing little or no “smart” video performance, which at most, enhances the view for human inspection.
However, human inspectors have to be trained and tend to become habituated to their setting, which often reduces their vigilance, and the likelihood of spotting a particular potential problem, over time. Also, given the wide range of different vehicle designs, even in reasonably standardized industries such as railroad cars, it becomes difficult for an inspector to reliably say that something they see in a mirror or in an undercarriage view is actually an anomaly. If an inspector is too cautious, many vehicles are stopped for no purpose, holding up the flow of traffic. However, if the inspector is too lenient, contraband may enter/exit the installation unchallenged, which can prove disastrous.
To date, all of the few known commercial systems providing some “intelligent” or “smart” video analysis, only analyze visible-light images for differences and/or specific shapes. However, the lack of three-dimensional profile information on the vehicle renders these systems, similar to the individuals viewing the images, readily deceived by camouflage, e.g., matching the color and texture of the perceived exterior to the expected color and texture.
Sensor and/or data fusion is the combination (fusing) of information across time, space and/or modalities, which can be analyzed to obtain various types of information. For example, the passage of an object can be detected by noting an amount of light reaching a sensor was a relatively high level, abruptly went low, then returned to a high level (data fusion across time). The passage can be correlated with another event, such as the arrival of another object, by noting the same pattern occurring at another similar sensor located elsewhere (data fusion across space). Further, the object can be identified as a motor vehicle by noting that the passage was coincident with the identification of a motor vehicle based on acoustic data acquired by a co-located acoustic sensor (data fusion across different sensor modalities).